Method for recommending seeding rate for corn seed using seed type and sowing row width

ABSTRACT

A computer system and computer-implemented techniques for determining and presenting improved seeding rate recommendations for sowing hybrid seeds in a field is provided. In an embodiment, determining and presenting seeding rate recommendations for a field may be accomplished using a server computer system that receives over a digital communication network, electronic digital data representing hybrid seed properties, including hybrid seed type, and sowing row width. Using digitally programmed seeding query logic, within the server computer system, receiving digital data representing planting parameters including hybrid seed type information and sowing row width. The seeding query logic then retrieves a set of one or more seeding models from an electronic digital seeding data repository based upon the planting parameters. Each of the seeding model retrieved contain a regression model for the hybrid seed type modeling a relationship between plant yield and seeding rate on a specific field. Using mixture model logic, within the server computer system, generating an empirical mixture model in digital computer memory that represents a composite distribution of the set of one or more seeding models. The mixture model logic then generates an optimal seeding rate distribution dataset in digital computer memory based upon the empirical mixture model, where the optimal seeding rate distribution dataset represents the optimal seeding rate across all measure fields. Using optimal seeding rate recommendation logic, within the server computer system, calculating and presenting on a digital display device an optimal seeding rate recommendation that is based upon the optimal seeding rate distribution dataset.

BENEFIT CLAIM

This application is a Continuation of prior U.S. patent application Ser.No. 16/505,486 (Attorney Docket No. 60403-0540), filed Jul. 8, 2019,which is a Continuation of prior U.S. patent application Ser. No.14/885,886 (Attorney Docket No. 60403-0059), filed Oct. 16, 2015, nowissued as U.S. Pat. No. 10,342,174 on Jul. 9, 2019, the contents of eachof which are incorporated by reference for all purposes as if fully setforth herein. The applicants hereby rescind any disclaimer of claimscope in the parent applications or the prosecution history thereof andadvise the USPTO that the claims in this application may be broader thanany claim in the parent applications.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyright orrights whatsoever. © 2020 The Climate Corporation.

FIELD OF THE DISCLOSURE

The present disclosure relates to computer-implemented techniques forpredicting or recommending an optimal seeding rate for hybrid corn seedbased upon hybrid seed type and sowing row width.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

Seeding rate is one of many important agronomical management decisions acorn grower makes each year. Seeding rate refers to the number of seedsplanted in an acre of land. Seed costs may constitute up to 14% of agrower's total production cost per year. Therefore it is important todetermine an optimal seeding rate that produces that desired return forthe grower. Different types of corn plants may produce different yieldsbased upon population density. As a result the hybrid seed type mayaffect the relationship between seeding rate and yield.

In general, corn plants within a field share resources and as a resultmore densely populated corn plants produce smaller ears than corn plantsthat are more spread out. Corn response to increased seeding rate isdependent on a biologically complex process which involves bothvegetative and reproductive growth and affects grain yield throughvarious components such as number of ears per plant, number of kernelsin an ear and kernel weight. As a result, the final yield is a trade-offbetween more plants in an area and the decreased yield per plant due tointensified inter-plant competition. Seeding rate is also affected bysoil productivity, weather conditions, and sowing row width. Determiningoptimal seeding rate for a grower may depend on hybrid seed varietiesand planting strategies.

SUMMARY

The appended claims may serve as a summary of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using agronomic data provided by one or more datasources.

FIG. 4 is a block diagram that illustrates a computer system 400 uponwhich an embodiment of the invention may be implemented.

FIG. 5 depicts an example programmed algorithm or process fordetermining a recommended seeding rate for a specific hybrid seed andsowing row width of corn planted at a specific geo-location.

FIG. 6 depicts an example programmed algorithm or process by whichseeding model logic is used to create a seeding model for a specifictest field.

FIG. 7A and FIG. 7B depict graphical relationships between yield andseeding rate.

FIG. 8 depicts an example embodiment of a timeline view for data entry.

FIG. 9 depicts an example embodiment of a spreadsheet view for dataentry.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that embodiments may be practiced without these specific details. Inother instances, well-known structures and devices are shown in blockdiagram form in order to avoid unnecessarily obscuring the presentdisclosure. Embodiments are disclosed in sections according to thefollowing outline:

1. GENERAL OVERVIEW

2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM

-   -   2.1. STRUCTURAL OVERVIEW    -   2.2. APPLICATION PROGRAM OVERVIEW    -   2.3. DATA INGEST TO THE COMPUTER SYSTEM    -   2.4. PROCESS OVERVIEW—AGRONOMIC MODEL TRAINING    -   2.5. SEEDING RATE RECOMMENDATION SUBSYSTEM    -   2.6. IMPLEMENTATION EXAMPLE—HARDWARE OVERVIEW

3. FUNCTIONAL OVERVIEW

-   -   3.1. SEEDING MODEL PARAMETER QUERY LOGIC    -   3.2. MIXTURE MODEL LOGIC    -   3.3. OPTIMAL SEEDING RATE RECOMMENDATION LOGIC    -   3.4. SEEDING MODEL LOGIC

1. GENERAL OVERVIEW

A computer system and computer-implemented techniques are provided fordetermining and presenting improved seeding rate recommendations forsowing hybrid seeds in a field. In an embodiment, determining andpresenting seeding rate recommendations for a field may be accomplishedusing a server computer system that is configured and programmed toreceive over a digital communication network, electronic digital datarepresenting hybrid seed properties, including hybrid seed type, andsowing row width. Using digitally programmed seeding query logic, thecomputer system is programmed to receive digital data representingplanting parameters including hybrid seed type information and sowingrow width. Using the seeding query logic, the system is programmed toretrieve a set of one or more seeding models from an electronic digitalseeding data repository based upon the planting parameters. Each of theseeding models retrieved contain a regression model that models therelationship between plant yield and seeding rate on a specific fieldtested with the hybrid seed type. “Model,” in this context, refers to aset of computer executable instructions and associated data that can beinvoked, called, executed, resolved or calculated to yield digitallystored output data based upon input data that is received in electronicdigital form. It is convenient, at times, in this disclosure to specifya model using one or more mathematical equations, but any such model isintended to be implemented in programmed computer-executableinstructions that are stored in memory with associated data.

Using mixture model logic, the computer system is programmed to generatean empirical mixture model in digital computer memory that represents acomposite distribution of the set of one or more seeding models. Usingthe mixture model logic, the computer system is programmed to generatean optimal seeding rate distribution dataset in digital computer memorybased upon the empirical mixture model, where the optimal seeding ratedistribution dataset represents the distribution of optimal seedingrates across all measure fields.

Using optimal seeding rate recommendation logic, the computer system isprogrammed to calculate and present on a digital display device anoptimal seeding rate recommendation that is based upon the optimalseeding rate distribution dataset.

2. Example Agricultural Intelligence Computer System

2.1 Structural Overview

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate. In oneembodiment, a user 102 owns, operates or possesses a field managercomputing device 104 in a field location or associated with a fieldlocation such as a field intended for agricultural activities or amanagement location for one or more agricultural fields. The fieldmanager computer device 104 is programmed or configured to provide fielddata 106 to an agricultural intelligence computer system 130 via one ormore networks 109.

Examples of field data 106 include (a) identification data (for example,acreage, field name, field identifiers, geographic identifiers, boundaryidentifiers, crop identifiers, and any other suitable data that may beused to identify farm land, such as a common land unit (CLU), lot andblock number, a parcel number, geographic coordinates and boundaries,Farm Serial Number (FSN), farm number, tract number, field number,section, township, and/or range), (b) harvest data (for example, croptype, crop variety, crop rotation, whether the crop is grownorganically, harvest date, Actual Production History (APH), expectedyield, yield, crop price, crop revenue, grain moisture, tillagepractice, and previous growing season information), (c) soil data (forexample, type, composition, pH, organic matter (OM), cation exchangecapacity (CEC)), (d) planting data (for example, planting date, seed(s)type, relative maturity (RM) of planted seed(s), seed population), (e)fertilizer data (for example, nutrient type (Nitrogen, Phosphorous,Potassium), application type, application date, amount, source, method),(f) pesticide data (for example, pesticide, herbicide, fungicide, othersubstance or mixture of substances intended for use as a plantregulator, defoliant, or desiccant, application date, amount, source,method), (g) irrigation data (for example, application date, amount,source, method), (h) weather data (for example, precipitation,temperature, wind, forecast, pressure, visibility, clouds, heat index,dew point, humidity, snow depth, air quality, sunrise, sunset), (i)imagery data (for example, imagery and light spectrum information froman agricultural apparatus sensor, camera, computer, smartphone, tablet,unmanned aerial vehicle, planes or satellite), (j) scouting observations(photos, videos, free form notes, voice recordings, voicetranscriptions, weather conditions (temperature, precipitation (currentand over time), soil moisture, crop growth stage, wind velocity,relative humidity, dew point, black layer)), and (k) soil, seed, cropphenology, pest and disease reporting, and predictions sources anddatabases.

An data server computer 108 is communicatively coupled to agriculturalintelligence computer system 130 and is programmed or configured to sendexternal data 110 to agricultural intelligence computer system 130 viathe network(s) 109. The external data server computer 108 may be ownedor operated by the same legal person or entity as the agriculturalintelligence computer system 130, or by a different person or entitysuch as a government agency, non-governmental organization (NGO), and/ora private data service provider. Examples of external data includeweather data, imagery data, soil data, or statistical data relating tocrop yields, among others. External data 110 may consist of the sametype of information as field data 106. In some embodiments, the externaldata 110 is provided by an external data server 108 owned by the sameentity that owns and/or operates the agricultural intelligence computersystem 130. For example, the agricultural intelligence computer system130 may include a data server focused exclusively on a type of thatmight otherwise be obtained from third party sources, such as weatherdata. In some embodiments, an external data server 108 may actually beincorporated within the system 130.

An agricultural apparatus 111 has one or more remote sensors 112 fixedthereon, which sensors are communicatively coupled either directly orindirectly via agricultural apparatus 111 to the agriculturalintelligence computer system 130 and are programmed or configured tosend sensor data to agricultural intelligence computer system 130.Examples of agricultural apparatus 111 include tractors, combines,harvesters, planters, trucks, fertilizer equipment, unmanned aerialvehicles, and any other item of physical machinery or hardware,typically mobile machinery, and which may be used in tasks associatedwith agriculture. In some embodiments, a single unit of apparatus 111may comprise a plurality of sensors 112 that are coupled locally in anetwork on the apparatus; controller area network (CAN) is example ofsuch a network that can be installed in combines or harvesters.Application controller 114 is communicatively coupled to agriculturalintelligence computer system 130 via the network(s) 109 and isprogrammed or configured to receive one or more scripts to control anoperating parameter of an agricultural vehicle or implement from theagricultural intelligence computer system 130. For instance, acontroller area network (CAN) bus interface may be used to enablecommunications from the agricultural intelligence computer system 130 tothe agricultural apparatus 111, such as how the CLIMATE FIELDVIEW DRIVE,available from The Climate Corporation, San Francisco, Calif., is used.Sensor data may consist of the same type of information as field data106.

The apparatus 111 may comprise a cab computer 115 that is programmedwith a cab application, which may comprise a version or variant of themobile application for device 104 that is further described in othersections herein. In an embodiment, cab computer 115 comprises a compactcomputer, often a tablet-sized computer or smartphone, with a colorgraphical screen display that is mounted within an operator's cab of theapparatus 111. Cab computer 115 may implement some or all of theoperations and functions that are described further herein for themobile computer device 104.

The network(s) 109 broadly represent any combination of one or more datacommunication networks including local area networks, wide areanetworks, internetworks or internets, using any of wireline or wirelesslinks, including terrestrial or satellite links. The network(s) may beimplemented by any medium or mechanism that provides for the exchange ofdata between the various elements of FIG. 1. The various elements ofFIG. 1 may also have direct (wired or wireless) communications links.The sensors 112, controller 114, external data server computer 108, andother elements of the system each comprise an interface compatible withthe network(s) 109 and are programmed or configured to use standardizedprotocols for communication across the networks such as TCP/IP,Bluetooth, CAN protocol and higher-layer protocols such as HTTP, TLS,and the like.

Agricultural intelligence computer system 130 is programmed orconfigured to receive field data 106 from field manager computing device104, external data 110 from external data server computer 108, andsensor data from remote sensor 112. Agricultural intelligence computersystem 130 may be further configured to host, use or execute one or morecomputer programs, other software elements, digitally programmed logicsuch as FPGAs or ASICs, or any combination thereof to performtranslation and storage of data values, construction of digital modelsof one or more crops on one or more fields, generation ofrecommendations and notifications, and generation and sending of scriptsto application controller 114, in the manner described further in othersections of this disclosure.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises a communication layer 132, presentationlayer 134, data management layer 140, hardware/virtualization layer 150,and model and field data repository 160. “Layer,” in this context,refers to any combination of electronic digital interface circuits,microcontrollers, firmware such as drivers, and/or computer programs orother software elements.

Communication layer 132 may be programmed or configured to performinput/output interfacing functions including sending requests to fieldmanager computing device 104, external data server computer 108, andremote sensor 112 for field data, external data, and sensor datarespectively. Communication layer 132 may be programmed or configured tosend the received data to model and field data repository 160 to bestored as field data 106.

Presentation layer 134 may be programmed or configured to generate agraphical user interface (GUI) to be displayed on field managercomputing device 104, cab computer 115 or other computers that arecoupled to the system 130 through the network 109. The GUI may comprisecontrols for inputting data to be sent to agricultural intelligencecomputer system 130, generating requests for models and/orrecommendations, and/or displaying recommendations, notifications,models, and other field data.

Data management layer 140 may be programmed or configured to manage readoperations and write operations involving the repository 160 and otherfunctional elements of the system, including queries and result setscommunicated between the functional elements of the system and therepository. Examples of data management layer 140 include JDBC, SQLserver interface code, and/or HADOOP interface code, among others.Repository 160 may comprise a database. As used herein, the term“database” may refer to either a body of data, a relational databasemanagement system (RDBMS), or to both. As used herein, a database maycomprise any collection of data including hierarchical databases,relational databases, flat file databases, object-relational databases,object oriented databases, and any other structured collection ofrecords or data that is stored in a computer system. Examples of RDBMS'sinclude, but are not limited to including, ORACLE®, MYSQL, IBM® DB2,MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQL databases. However, anydatabase may be used that enables the systems and methods describedherein.

When field data 106 is not provided directly to the agriculturalintelligence computer system via one or more agricultural machines oragricultural machine devices that interacts with the agriculturalintelligence computer system, the user may be prompted via one or moreuser interfaces on the user device (served by the agriculturalintelligence computer system) to input such information. In an exampleembodiment, the user may specify identification data by accessing a mapon the user device (served by the agricultural intelligence computersystem) and selecting specific CLUs that have been graphically shown onthe map. In an alternative embodiment, the user 102 may specifyidentification data by accessing a map on the user device (served by theagricultural intelligence computer system 130) and drawing boundaries ofthe field over the map. Such CLU selection or map drawings representgeographic identifiers. In alternative embodiments, the user may specifyidentification data by accessing field identification data (provided asshape files or in a similar format) from the U. S. Department ofAgriculture Farm Service Agency or other source via the user device andproviding such field identification data to the agriculturalintelligence computer system.

In an example embodiment, the agricultural intelligence computer system130 is programmed to generate and cause displaying a graphical userinterface comprising a data manager for data input. After one or morefields have been identified using the methods described above, the datamanager may provide one or more graphical user interface widgets whichwhen selected can identify changes to the field, soil, crops, tillage,or nutrient practices. The data manager may include a timeline view, aspreadsheet view, and/or one or more editable programs.

FIG. 8 depicts an example embodiment of a timeline view for data entry.Using the display depicted in FIG. 8, a user computer can input aselection of a particular field and a particular date for the additionof event. Events depicted at the top of the timeline include Nitrogen,Planting, Practices, and Soil. To add a nitrogen application event, auser computer may provide input to select the nitrogen tab. The usercomputer may then select a location on the timeline for a particularfield in order to indicate an application of nitrogen on the selectedfield. In response to receiving a selection of a location on thetimeline for a particular field, the data manager may display a dataentry overlay, allowing the user computer to input data pertaining tonitrogen applications, planting procedures, soil application, tillageprocedures, irrigation practices, or other information relating to theparticular field. For example, if a user computer selects a portion ofthe timeline and indicates an application of nitrogen, then the dataentry overlay may include fields for inputting an amount of nitrogenapplied, a date of application, a type of fertilizer used, and any otherinformation related to the application of nitrogen.

In an embodiment, the data manager provides an interface for creatingone or more programs. “Program,” in this context, refers to a set ofdata pertaining to nitrogen applications, planting procedures, soilapplication, tillage procedures, irrigation practices, or otherinformation that may be related to one or more fields, and that can bestored in digital data storage for reuse as a set in other operations.After a program has been created, it may be conceptually applied to oneor more fields and references to the program may be stored in digitalstorage in association with data identifying the fields. Thus, insteadof manually entering identical data relating to the same nitrogenapplications for multiple different fields, a user computer may create aprogram that indicates a particular application of nitrogen and thenapply the program to multiple different fields. For example, in thetimeline view of FIG. 8, the top two timelines have the “Fall applied”program selected, which includes an application of 150 lbs N/ac in earlyApril. The data manager may provide an interface for editing a program.In an embodiment, when a particular program is edited, each field thathas selected the particular program is edited. For example, in FIG. 8,if the “Fall applied” program is edited to reduce the application ofnitrogen to 130 lbs N/ac, the top two fields may be updated with areduced application of nitrogen based on the edited program.

In an embodiment, in response to receiving edits to a field that has aprogram selected, the data manager removes the correspondence of thefield to the selected program. For example, if a nitrogen application isadded to the top field in FIG. 8, the interface may update to indicatethat the “Fall applied” program is no longer being applied to the topfield. While the nitrogen application in early April may remain, updatesto the “Fall applied” program would not alter the April application ofnitrogen.

FIG. 9 depicts an example embodiment of a spreadsheet view for dataentry. Using the display depicted in FIG. 9, a user can create and editinformation for one or more fields. The data manager may includespreadsheets for inputting information with respect to Nitrogen,Planting, Practices, and Soil as depicted in FIG. 9. To edit aparticular entry, a user computer may select the particular entry in thespreadsheet and update the values. For example, FIG. 9 depicts anin-progress update to a target yield value for the second field.Additionally, a user computer may select one or more fields in order toapply one or more programs. In response to receiving a selection of aprogram for a particular field, the data manager may automaticallycomplete the entries for the particular field based on the selectedprogram. As with the timeline view, the data manager may update theentries for each field associated with a particular program in responseto receiving an update to the program. Additionally, the data managermay remove the correspondence of the selected program to the field inresponse to receiving an edit to one of the entries for the field.

In an embodiment, model and field data is stored in model and field datarepository 160. Model data comprises data models created for one or morefields. For example, a crop model may include a digitally constructedmodel of the development of a crop on the one or more fields. “Model,”in this context, refers to an electronic digitally stored set ofexecutable instructions and data values, associated with one another,which are capable of receiving and responding to a programmatic or otherdigital call, invocation, or request for resolution based upon specifiedinput values, to yield one or more stored output values that can serveas the basis of computer-implemented recommendations, output datadisplays, or machine control, among other things. Persons of skill inthe field find it convenient to express models using mathematicalequations, but that form of expression does not confine the modelsdisclosed herein to abstract concepts; instead, each model herein has apractical application in a computer in the form of stored executableinstructions and data that implement the model using the computer. Themodel data may include a model of past events on the one or more fields,a model of the current status of the one or more fields, and/or a modelof predicted events on the one or more fields. Model and field data maybe stored in data structures in memory, rows in a database table, inflat files or spreadsheets, or other forms of stored digital data.

Hardware/virtualization layer 150 comprises one or more centralprocessing units (CPUs), memory controllers, and other devices,components, or elements of a computer system such as volatile ornon-volatile memory, non-volatile storage such as disk, and I/O devicesor interfaces as illustrated and described, for example, in connectionwith FIG. 4. The layer 150 also may comprise programmed instructionsthat are configured to support virtualization, containerization, orother technologies.

For purposes of illustrating a clear example, FIG. 1 shows a limitednumber of instances of certain functional elements. However, in otherembodiments, there may be any number of such elements. For example,embodiments may use thousands or millions of different mobile computingdevices 104 associated with different users. Further, the system 130and/or external data server computer 108 may be implemented using two ormore processors, cores, clusters, or instances of physical machines orvirtual machines, configured in a discrete location or co-located withother elements in a datacenter, shared computing facility or cloudcomputing facility.

2.2. Application Program Overview

In an embodiment, the implementation of the functions described hereinusing one or more computer programs or other software elements that areloaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Further, each of the flow diagrams thatare described further herein may serve, alone or in combination with thedescriptions of processes and functions in prose herein, as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

In an embodiment, user 102 interacts with agricultural intelligencecomputer system 130 using field manager computing device 104 configuredwith an operating system and one or more application programs or apps;the field manager computing device 104 also may interoperate with theagricultural intelligence computer system independently andautomatically under program control or logical control and direct userinteraction is not always required. Field manager computing device 104broadly represents one or more of a smart phone, PDA, tablet computingdevice, laptop computer, desktop computer, workstation, or any othercomputing device capable of transmitting and receiving information andperforming the functions described herein. Field manager computingdevice 104 may communicate via a network using a mobile applicationstored on field manager computing device 104, and in some embodiments,the device may be coupled using a cable 113 or connector to the sensor112 and/or controller 114. A particular user 102 may own, operate orpossess and use, in connection with system 130, more than one fieldmanager computing device 104 at a time.

The mobile application may provide client-side functionality, via thenetwork to one or more mobile computing devices. In an exampleembodiment, field manager computing device 104 may access the mobileapplication via a web browser or a local client application or app.Field manager computing device 104 may transmit data to, and receivedata from, one or more front-end servers, using web-based protocols orformats such as HTTP, XML, and/or JSON, or app-specific protocols. In anexample embodiment, the data may take the form of requests and userinformation input, such as field data, into the mobile computing device.In some embodiments, the mobile application interacts with locationtracking hardware and software on field manager computing device 104which determines the location of field manager computing device 104using standard tracking techniques such as multilateration of radiosignals, the global positioning system (GPS), WiFi positioning systems,or other methods of mobile positioning. In some cases, location data orother data associated with the device 104, user 102, and/or useraccount(s) may be obtained by queries to an operating system of thedevice or by requesting an app on the device to obtain data from theoperating system.

In an embodiment, field manager computing device 104 sends field data106 to agricultural intelligence computer system 130 comprising orincluding, but not limited to, data values representing one or more of:a geographical location of the one or more fields, tillage informationfor the one or more fields, crops planted in the one or more fields, andsoil data extracted from the one or more fields. Field manager computingdevice 104 may send field data 106 in response to user input from user102 specifying the data values for the one or more fields. Additionally,field manager computing device 104 may automatically send field data 106when one or more of the data values becomes available to field managercomputing device 104. For example, field manager computing device 104may be communicatively coupled to remote sensor 112 and/or applicationcontroller 114. In response to receiving data indicating thatapplication controller 114 released water onto the one or more fields,field manager computing device 104 may send field data 106 toagricultural intelligence computer system 130 indicating that water wasreleased on the one or more fields. Field data 106 identified in thisdisclosure may be input and communicated using electronic digital datathat is communicated between computing devices using parameterized URLsover HTTP, or another suitable communication or messaging protocol.

A commercial example of the mobile application is CLIMATE FIELDVIEW,commercially available from The Climate Corporation, San Francisco,Calif. The CLIMATE FIELDVIEW application, or other applications, may bemodified, extended, or adapted to include features, functions, andprogramming that have not been disclosed earlier than the filing date ofthis disclosure. In one embodiment, the mobile application comprises anintegrated software platform that allows a grower to make fact-baseddecisions for their operation because it combines historical data aboutthe grower's fields with any other data that the grower wishes tocompare. The combinations and comparisons may be performed in real timeand are based upon scientific models that provide potential scenarios topermit the grower to make better, more informed decisions.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution. In FIG. 2, each named element represents a regionof one or more pages of RAM or other main memory, or one or more blocksof disk storage or other non-volatile storage, and the programmedinstructions within those regions. In one embodiment, in view (a), amobile computer application 200 comprises account-fields-dataingestion-sharing instructions 202, overview and alert instructions 204,digital map book instructions 206, seeds and planting instructions 208,nitrogen instructions 210, weather instructions 212, field healthinstructions 214, and performance instructions 216.

In one embodiment, a mobile computer application 200 comprisesaccount-fields-data ingestion-sharing instructions 202 which areprogrammed to receive, translate, and ingest field data from third partysystems via manual upload or APIs. Data types may include fieldboundaries, yield maps, as-planted maps, soil test results, as-appliedmaps, and/or management zones, among others. Data formats may includeshape files, native data formats of third parties, and/or farmmanagement information system (FMIS) exports, among others. Receivingdata may occur via manual upload, e-mail with attachment, external APIsthat push data to the mobile application, or instructions that call APIsof external systems to pull data into the mobile application. In oneembodiment, mobile computer application 200 comprises a data inbox. Inresponse to receiving a selection of the data inbox, the mobile computerapplication 200 may display a graphical user interface for manuallyuploading data files and importing uploaded files to a data manager.

In one embodiment, digital map book instructions 206 comprise field mapdata layers stored in device memory and are programmed with datavisualization tools and geospatial field notes. This provides growerswith convenient information close at hand for reference, logging andvisual insights into field performance. In one embodiment, overview andalert instructions 204 are programmed to provide an operation-wide viewof what is important to the grower, and timely recommendations to takeaction or focus on particular issues. This permits the grower to focustime on what needs attention, to save time and preserve yield throughoutthe season. In one embodiment, seeds and planting instructions 208 areprogrammed to provide tools for seed selection, hybrid placement, andscript creation, including variable rate (VR) script creation, basedupon scientific models and empirical data. This enables growers tomaximize yield or return on investment through optimized seed purchase,placement and population.

In one embodiment, script generation instructions 205 are programmed toprovide an interface for generating scripts, including variable rate(VR) fertility scripts. The interface enables growers to create scriptsfor field implements, such as nutrient applications, planting, andirrigation. For example, a planting script interface may comprise toolsfor identifying a type of seed for planting. Upon receiving a selectionof the seed type, mobile computer application 200 may display one ormore fields broken into soil zones along with a panel identifying eachsoil zone and a soil name, texture, and drainage for each zone. Mobilecomputer application 200 may also display tools for editing or creatingsuch, such as graphical tools for drawing soil zones over a map of oneor more fields. Planting procedures may be applied to all soil zones ordifferent planting procedures may be applied to different subsets ofsoil zones. When a script is created, mobile computer application 200may make the script available for download in a format readable by anapplication controller, such as an archived or compressed format.Additionally and/or alternatively, a script may be sent directly to cabcomputer 115 from mobile computer application 200 and/or uploaded to oneor more data servers and stored for further use. In one embodiment,nitrogen instructions 210 are programmed to provide tools to informnitrogen decisions by visualizing the availability of nitrogen to crops.This enables growers to maximize yield or return on investment throughoptimized nitrogen application during the season. Example programmedfunctions include displaying images such as SSURGO images to enabledrawing of application zones and/or images generated from subfield soildata, such as data obtained from sensors, at a high spatial resolution(as fine as 10 meters or smaller because of their proximity to thesoil); upload of existing grower-defined zones; providing an applicationgraph and/or a map to enable tuning application(s) of nitrogen acrossmultiple zones; output of scripts to drive machinery; tools for massdata entry and adjustment; and/or maps for data visualization, amongothers. “Mass data entry,” in this context, may mean entering data onceand then applying the same data to multiple fields that have beendefined in the system; example data may include nitrogen applicationdata that is the same for many fields of the same grower, but such massdata entry applies to the entry of any type of field data into themobile computer application 200. For example, nitrogen instructions 210may be programmed to accept definitions of nitrogen planting andpractices programs and to accept user input specifying to apply thoseprograms across multiple fields. “Nitrogen planting programs,” in thiscontext, refers to a stored, named set of data that associates: a name,color code or other identifier, one or more dates of application, typesof material or product for each of the dates and amounts, method ofapplication or incorporation such as injected or knifed in, and/oramounts or rates of application for each of the dates, crop or hybridthat is the subject of the application, among others. “Nitrogenpractices programs,” in this context, refers to a stored, named set ofdata that associates: a practices name; a previous crop; a tillagesystem; a date of primarily tillage; one or more previous tillagesystems that were used; one or more indicators of application type, suchas manure, that were used. Nitrogen instructions 210 also may beprogrammed to generate and cause displaying a nitrogen graph, whichindicates projections of plant use of the specified nitrogen and whethera surplus or shortfall is predicted; in some embodiments, differentcolor indicators may signal a magnitude of surplus or magnitude ofshortfall. In one embodiment, a nitrogen graph comprises a graphicaldisplay in a computer display device comprising a plurality of rows,each row associated with and identifying a field; data specifying whatcrop is planted in the field, the field size, the field location, and agraphic representation of the field perimeter; in each row, a timelineby month with graphic indicators specifying each nitrogen applicationand amount at points correlated to month names; and numeric and/orcolored indicators of surplus or shortfall, in which color indicatesmagnitude.

In one embodiment, the nitrogen graph may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen graph. The user may then use his optimized nitrogen graph andthe related nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. Nitrogeninstructions 210 also may be programmed to generate and cause displayinga nitrogen map, which indicates projections of plant use of thespecified nitrogen and whether a surplus or shortfall is predicted; insome embodiments, different color indicators may signal a magnitude ofsurplus or magnitude of shortfall. The nitrogen map may displayprojections of plant use of the specified nitrogen and whether a surplusor shortfall is predicted for different times in the past and the future(such as daily, weekly, monthly or yearly) using numeric and/or coloredindicators of surplus or shortfall, in which color indicates magnitude.In one embodiment, the nitrogen map may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen map, such as to obtain a preferred amount of surplus toshortfall. The user may then use his optimized nitrogen map and therelated nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. In otherembodiments, similar instructions to the nitrogen instructions 210 couldbe used for application of other nutrients (such as phosphorus andpotassium) application of pesticide, and irrigation programs.

In one embodiment, weather instructions 212 are programmed to providefield-specific recent weather data and forecasted weather information.This enables growers to save time and have an efficient integrateddisplay with respect to daily operational decisions.

In one embodiment, field health instructions 214 are programmed toprovide timely remote sensing images highlighting in-season cropvariation and potential concerns. Example programmed functions includecloud checking, to identify possible clouds or cloud shadows;determining nitrogen indices based on field images; graphicalvisualization of scouting layers, including, for example, those relatedto field health, and viewing and/or sharing of scouting notes; and/ordownloading satellite images from multiple sources and prioritizing theimages for the grower, among others.

In one embodiment, performance instructions 216 are programmed toprovide reports, analysis, and insight tools using on-farm data forevaluation, insights and decisions. This enables the grower to seekimproved outcomes for the next year through fact-based conclusions aboutwhy return on investment was at prior levels, and insight intoyield-limiting factors. The performance instructions 216 may beprogrammed to communicate via the network(s) 109 to back-end analyticsprograms executed at agricultural intelligence computer system 130and/or external data server computer 108 and configured to analyzemetrics such as yield, hybrid, population, SSURGO, soil tests, orelevation, among others. Programmed reports and analysis may includeyield variability analysis, benchmarking of yield and other metricsagainst other growers based on anonymized data collected from manygrowers, or data for seeds and planting, among others.

Applications having instructions configured in this way may beimplemented for different computing device platforms while retaining thesame general user interface appearance. For example, the mobileapplication may be programmed for execution on tablets, smartphones, orserver computers that are accessed using browsers at client computers.Further, the mobile application as configured for tablet computers orsmartphones may provide a full app experience or a cab app experiencethat is suitable for the display and processing capabilities of cabcomputer 115. For example, referring now to view (b) of FIG. 2, in oneembodiment a cab computer application 220 may comprise maps-cabinstructions 222, remote view instructions 224, data collect andtransfer instructions 226, machine alerts instructions 228, scripttransfer instructions 230, and scouting-cab instructions 232. The codebase for the instructions of view (b) may be the same as for view (a)and executables implementing the code may be programmed to detect thetype of platform on which they are executing and to expose, through agraphical user interface, only those functions that are appropriate to acab platform or full platform. This approach enables the system torecognize the distinctly different user experience that is appropriatefor an in-cab environment and the different technology environment ofthe cab. The maps-cab instructions 222 may be programmed to provide mapviews of fields, farms or regions that are useful in directing machineoperation. The remote view instructions 224 may be programmed to turnon, manage, and provide views of machine activity in real-time or nearreal-time to other computing devices connected to the system 130 viawireless networks, wired connectors or adapters, and the like. The datacollect and transfer instructions 226 may be programmed to turn on,manage, and provide transfer of data collected at machine sensors andcontrollers to the system 130 via wireless networks, wired connectors oradapters, and the like. The machine alerts instructions 228 may beprogrammed to detect issues with operations of the machine or tools thatare associated with the cab and generate operator alerts. The scripttransfer instructions 230 may be configured to transfer in scripts ofinstructions that are configured to direct machine operations or thecollection of data. The scouting-cab instructions 230 may be programmedto display location-based alerts and information received from thesystem 130 based on the location of the agricultural apparatus 111 orsensors 112 in the field and ingest, manage, and provide transfer oflocation-based scouting observations to the system 130 based on thelocation of the agricultural apparatus 111 or sensors 112 in the field.

2.3. Data Ingest to the Computer System

In an embodiment, external data server computer 108 stores external data110, including soil data representing soil composition for the one ormore fields and weather data representing temperature and precipitationon the one or more fields. The weather data may include past and presentweather data as well as forecasts for future weather data. In anembodiment, external data server computer 108 comprises a plurality ofservers hosted by different entities. For example, a first server maycontain soil composition data while a second server may include weatherdata. Additionally, soil composition data may be stored in multipleservers. For example, one server may store data representing percentageof sand, silt, and clay in the soil while a second server may store datarepresenting percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor 112 comprises one or more sensors thatare programmed or configured to produce one or more observations. Remotesensor 112 may be aerial sensors, such as satellites, vehicle sensors,planting equipment sensors, tillage sensors, fertilizer or insecticideapplication sensors, harvester sensors, and any other implement capableof receiving data from the one or more fields. In an embodiment,application controller 114 is programmed or configured to receiveinstructions from agricultural intelligence computer system 130.Application controller 114 may also be programmed or configured tocontrol an operating parameter of an agricultural vehicle or implement.For example, an application controller may be programmed or configuredto control an operating parameter of a vehicle, such as a tractor,planting equipment, tillage equipment, fertilizer or insecticideequipment, harvester equipment, or other farm implements such as a watervalve. Other embodiments may use any combination of sensors andcontrollers, of which the following are merely selected examples.

The system 130 may obtain or ingest data under user 102 control, on amass basis from a large number of growers who have contributed data to ashared database system. This form of obtaining data may be termed“manual data ingest” as one or more user-controlled computer operationsare requested or triggered to obtain data for use by the system 130. Asan example, the CLIMATE FIELDVIEW application, commercially availablefrom The Climate Corporation, San Francisco, Calif., may be operated toexport data to system 130 for storing in the repository 160.

For example, seed monitor systems can both control planter apparatuscomponents and obtain planting data, including signals from seed sensorsvia a signal harness that comprises a CAN backbone and point-to-pointconnections for registration and/or diagnostics. Seed monitor systemscan be programmed or configured to display seed spacing, population andother information to the user via the cab computer 115 or other deviceswithin the system 130. Examples are disclosed in U.S. Pat. No. 8,738,243and US Pat. Pub. 20150094916, and the present disclosure assumesknowledge of those other patent disclosures.

Likewise, yield monitor systems may contain yield sensors for harvesterapparatus that send yield measurement data to the cab computer 115 orother devices within the system 130. Yield monitor systems may utilizeone or more remote sensors 112 to obtain grain moisture measurements ina combine or other harvester and transmit these measurements to the uservia the cab computer 115 or other devices within the system 130.

In an embodiment, examples of sensors 112 that may be used with anymoving vehicle or apparatus of the type described elsewhere hereininclude kinematic sensors and position sensors. Kinematic sensors maycomprise any of speed sensors such as radar or wheel speed sensors,accelerometers, or gyros. Position sensors may comprise GPS receivers ortransceivers, or WiFi-based position or mapping apps that are programmedto determine location based upon nearby WiFi hotspots, among others.

In an embodiment, examples of sensors 112 that may be used with tractorsor other moving vehicles include engine speed sensors, fuel consumptionsensors, area counters or distance counters that interact with GPS orradar signals, PTO (power take-off) speed sensors, tractor hydraulicssensors configured to detect hydraulics parameters such as pressure orflow, and/or and hydraulic pump speed, wheel speed sensors or wheelslippage sensors. In an embodiment, examples of controllers 114 that maybe used with tractors include hydraulic directional controllers,pressure controllers, and/or flow controllers; hydraulic pump speedcontrollers; speed controllers or governors; hitch position controllers;or wheel position controllers provide automatic steering.

In an embodiment, examples of sensors 112 that may be used with seedplanting equipment such as planters, drills, or air seeders include seedsensors, which may be optical, electromagnetic, or impact sensors;downforce sensors such as load pins, load cells, pressure sensors; soilproperty sensors such as reflectivity sensors, moisture sensors,electrical conductivity sensors, optical residue sensors, or temperaturesensors; component operating criteria sensors such as planting depthsensors, downforce cylinder pressure sensors, seed disc speed sensors,seed drive motor encoders, seed conveyor system speed sensors, or vacuumlevel sensors; or pesticide application sensors such as optical or otherelectromagnetic sensors, or impact sensors. In an embodiment, examplesof controllers 114 that may be used with such seed planting equipmentinclude: toolbar fold controllers, such as controllers for valvesassociated with hydraulic cylinders; downforce controllers, such ascontrollers for valves associated with pneumatic cylinders, airbags, orhydraulic cylinders, and programmed for applying downforce to individualrow units or an entire planter frame; planting depth controllers, suchas linear actuators; metering controllers, such as electric seed meterdrive motors, hydraulic seed meter drive motors, or swath controlclutches; hybrid selection controllers, such as seed meter drive motors,or other actuators programmed for selectively allowing or preventingseed or an air-seed mixture from delivering seed to or from seed metersor central bulk hoppers; metering controllers, such as electric seedmeter drive motors, or hydraulic seed meter drive motors; seed conveyorsystem controllers, such as controllers for a belt seed deliveryconveyor motor; marker controllers, such as a controller for a pneumaticor hydraulic actuator; or pesticide application rate controllers, suchas metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors 112 that may be used with tillageequipment include position sensors for tools such as shanks or discs;tool position sensors for such tools that are configured to detectdepth, gang angle, or lateral spacing; downforce sensors; or draft forcesensors. In an embodiment, examples of controllers 114 that may be usedwith tillage equipment include downforce controllers or tool positioncontrollers, such as controllers configured to control tool depth, gangangle, or lateral spacing.

In an embodiment, examples of sensors 112 that may be used in relationto apparatus for applying fertilizer, insecticide, fungicide and thelike, such as on-planter starter fertilizer systems, subsoil fertilizerapplicators, or fertilizer sprayers, include: fluid system criteriasensors, such as flow sensors or pressure sensors; sensors indicatingwhich spray head valves or fluid line valves are open; sensorsassociated with tanks, such as fill level sensors; sectional orsystem-wide supply line sensors, or row-specific supply line sensors; orkinematic sensors such as accelerometers disposed on sprayer booms. Inan embodiment, examples of controllers 114 that may be used with suchapparatus include pump speed controllers; valve controllers that areprogrammed to control pressure, flow, direction, PWM and the like; orposition actuators, such as for boom height, subsoiler depth, or boomposition.

In an embodiment, examples of sensors 112 that may be used withharvesters include yield monitors, such as impact plate strain gauges orposition sensors, capacitive flow sensors, load sensors, weight sensors,or torque sensors associated with elevators or augers, or optical orother electromagnetic grain height sensors; grain moisture sensors, suchas capacitive sensors; grain loss sensors, including impact, optical, orcapacitive sensors; header operating criteria sensors such as headerheight, header type, deck plate gap, feeder speed, and reel speedsensors; separator operating criteria sensors, such as concaveclearance, rotor speed, shoe clearance, or chaffer clearance sensors;auger sensors for position, operation, or speed; or engine speedsensors. In an embodiment, examples of controllers 114 that may be usedwith harvesters include header operating criteria controllers forelements such as header height, header type, deck plate gap, feederspeed, or reel speed; separator operating criteria controllers forfeatures such as concave clearance, rotor speed, shoe clearance, orchaffer clearance; or controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 that may be used with graincarts include weight sensors, or sensors for auger position, operation,or speed. In an embodiment, examples of controllers 114 that may be usedwith grain carts include controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 and controllers 114 may beinstalled in unmanned aerial vehicle (UAV) apparatus or “drones.” Suchsensors may include cameras with detectors effective for any range ofthe electromagnetic spectrum including visible light, infrared,ultraviolet, near-infrared (NIR), and the like; accelerometers;altimeters; temperature sensors; humidity sensors; pitot tube sensors orother airspeed or wind velocity sensors; battery life sensors; or radaremitters and reflected radar energy detection apparatus. Suchcontrollers may include guidance or motor control apparatus, controlsurface controllers, camera controllers, or controllers programmed toturn on, operate, obtain data from, manage and configure any of theforegoing sensors. Examples are disclosed in U.S. patent applicationSer. No. 14/831,165 and the present disclosure assumes knowledge of thatother patent disclosure.

In an embodiment, sensors 112 and controllers 114 may be affixed to soilsampling and measurement apparatus that is configured or programmed tosample soil and perform soil chemistry tests, soil moisture tests, andother tests pertaining to soil. For example, the apparatus disclosed inU.S. Pat. Nos. 8,767,194 and 8,712,148 may be used, and the presentdisclosure assumes knowledge of those patent disclosures.

2.4 Process Overview-Agronomic Model Training

In an embodiment, the agricultural intelligence computer system 130 isprogrammed or configured to create an agronomic model. In this context,an agronomic model is a data structure in memory of the agriculturalintelligence computer system 130 that comprises field data 106, such asidentification data and harvest data for one or more fields. Theagronomic model may also comprise calculated agronomic properties whichdescribe either conditions which may affect the growth of one or morecrops on a field, or properties of the one or more crops, or both.Additionally, an agronomic model may comprise recommendations based onagronomic factors such as crop recommendations, irrigationrecommendations, planting recommendations, and harvestingrecommendations. The agronomic factors may also be used to estimate oneor more crop related results, such as agronomic yield. The agronomicyield of a crop is an estimate of quantity of the crop that is produced,or in some examples the revenue or profit obtained from the producedcrop.

In an embodiment, the agricultural intelligence computer system 130 mayuse a preconfigured agronomic model to calculate agronomic propertiesrelated to currently received location and crop information for one ormore fields. The preconfigured agronomic model is based upon previouslyprocessed field data, including but not limited to, identification data,harvest data, fertilizer data, and weather data. The preconfiguredagronomic model may have been cross validated to ensure accuracy of themodel. Cross validation may include comparison to ground truthing thatcompares predicted results with actual results on a field, such as acomparison of precipitation estimate with a rain gauge at the samelocation or an estimate of nitrogen content with a soil samplemeasurement.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using field data provided by one or more data sources.FIG. 3 may serve as an algorithm or instructions for programming thefunctional elements of the agricultural intelligence computer system 130to perform the operations that are now described.

At block 305, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic data preprocessing offield data received from one or more data sources. The field datareceived from one or more data sources may be preprocessed for thepurpose of removing noise and distorting effects within the agronomicdata including measured outliers that would bias received field datavalues. Embodiments of agronomic data preprocessing may include, but arenot limited to, removing data values commonly associated with outlierdata values, specific measured data points that are known tounnecessarily skew other data values, data smoothing techniques used toremove or reduce additive or multiplicative effects from noise, andother filtering or data derivation techniques used to provide cleardistinctions between positive and negative data inputs.

At block 310, the agricultural intelligence computer system 130 isconfigured or programmed to perform data subset selection using thepreprocessed field data in order to identify datasets useful for initialagronomic model generation. The agricultural intelligence computersystem 130 may implement data subset selection techniques including, butnot limited to, a genetic algorithm method, an all subset models method,a sequential search method, a stepwise regression method, a particleswarm optimization method, and an ant colony optimization method. Forexample, a genetic algorithm selection technique uses an adaptiveheuristic search algorithm, based on evolutionary principles of naturalselection and genetics, to determine and evaluate datasets within thepreprocessed agronomic data.

At block 315, the agricultural intelligence computer system 130 isconfigured or programmed to implement field dataset evaluation. In anembodiment, a specific field dataset is evaluated by creating anagronomic model and using specific quality thresholds for the createdagronomic model. Agronomic models may be compared using cross validationtechniques including, but not limited to, root mean square error ofleave-one-out cross validation (RMSECV), mean absolute error, and meanpercentage error. For example, RMSECV can cross validate agronomicmodels by comparing predicted agronomic property values created by theagronomic model against historical agronomic property values collectedand analyzed. In an embodiment, the agronomic dataset evaluation logicis used as a feedback loop where agronomic datasets that do not meetconfigured quality thresholds are used during future data subsetselection steps (block 310).

At block 320, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic model creation basedupon the cross validated agronomic datasets. In an embodiment, agronomicmodel creation may implement multivariate regression techniques tocreate preconfigured agronomic data models.

At block 325, the agricultural intelligence computer system 130 isconfigured or programmed to store the preconfigured agronomic datamodels for future field data evaluation.

2.5 Seeding Rate Recommendation Subsystem

In an embodiment, the agricultural intelligence computer system 130,among other components, includes a seeding rate recommendation subsystem170. The seeding rate recommendation subsystem 170 is configured topredict an optimal seeding rate recommendation for hybrid corn seedbased upon hybrid seed type and sowing row width. The seeding raterecommendation subsystem 170 uses field data 106 and external data 110to create and retrieve digital seeding models related to multiplemeasured fields.

In an embodiment, the seeding rate recommendation subsystem 170 containsspecially configured logic including, but not limited to, seeding modelparameter query logic 173, mixture model logic 174, optimal seeding raterecommendation logic 175, and seeding model logic 176. Each of theforegoing elements is further described in structure and function inother sections herein. “Logic,” as used in FIG. 1, refers in at leastone embodiment to regions of main memory in the agriculturalintelligence computer system 130 into which programmed, executableinstructions have been loaded, and which instructions are configuredwhen executed to cause the computer to perform the functions that aredescribed herein for that logical element. For example, seeding modelparameter query logic 173 indicates a region of main memory into whichthe computer has loaded instructions, which when executed cause theperformance of the interface functions that are further describedherein. These elements of 1 also indirectly indicate how a typicalprogrammer or software engineer would organize the source code ofprograms that implement the functions that are described; the code maybe organized into logical modules, methods, subroutines, branches, orother units using an architecture corresponding to FIG. 1.

Seeding model parameter query logic 173 is generally configured orprogrammed to retrieve multiple digital seeding models related tomultiple measured fields based upon the input parameters received fromthe field data 106. A digital seeding model correlates crop yield toseeding rate based upon seeding strategies, such as seeding row width,for measured fields. Mixture Model logic 174 is generally configured orprogrammed to generate an empirical mixture model based upon seedingmodels of multiple measured fields. Optimal seeding rate recommendationlogic 175 is generally configured or programmed to determine an optimalseeding rate that maximizes yield or maximizes profit based upon thegenerated empirical mixture model. Seeding model logic 176 is generallyconfigured or programmed to generate a seeding model based upon multipletypes of field specific data for a single measured field. The seedingmodel includes, but is not limited to, regression models modeling thecorn yield to seeding rate, dataset of distributions for regressionparameters, and datasets that include multiple data points within ameasured field.

Each of the seeding model parameter query logic 173, mixture model logic174, optimal seeding rate recommendation logic 175, and seeding modellogic 176 may be implemented using one or more computer programs orother software elements that are loaded into and executed using one ormore general-purpose computers, logic implemented in field programmablegate arrays (FPGAs) or application-specific integrated circuits (ASICs).While FIG. 1 depicts seeding model parameter query logic 173, mixturemodel logic 174, optimal seeding rate recommendation logic 175, andseeding model logic 176 in one computing system, in various embodiments,logics 173, 174, 175, and 176 may operate on multiple computing systems.

In an embodiment, the implementation of the functions described hereinfor seeding model parameter query logic 173, mixture model logic 174,optimal seeding rate recommendation logic 175, and seeding model logic176 using one or more computer programs or other software elements thatare loaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Each of the items of logic in FIG. 1,and in all other drawing figures herein, may represent a region or setof one or more pages of main memory storing programmed instructionswhich when executed cause performing the process steps or algorithmsteps that are disclosed herein. Thus the logic elements do notrepresent mere abstractions but represent real pages of memory that havebeen loaded with executable instructions. Further, each of the flowdiagrams that are described further herein may serve as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

2.6 Implementation Example—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general purpose microprocessor.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 402for storing information and instructions to be executed by processor404. Main memory 406 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 404. Such instructions, when stored innon-transitory storage media accessible to processor 404, rendercomputer system 400 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 410. Volatile media includes dynamic memory, such asmain memory 406. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

3.0 Functional Overview

FIG. 5 is a flow diagram that depicts a process for determining anoptimal seeding rate for a specific hybrid seed and sowing row width ofcorn planted at a specific geo-location. FIG. 5 may be implemented, inone embodiment, by programming the elements of the agriculturalintelligence computer system 130 to perform the functions that aredescribed in this section, which may represent disclosure of analgorithm for computer implementation of the functions that aredescribed. For purposes of illustrating a clear example, FIG. 5 isdescribed in connection with certain elements of FIG. 1. However, otherembodiments of FIG. 5 may be practiced in many other contexts andreferences herein to units of FIG. 1 are merely examples that are notintended to limit the broader scope of FIG. 5.

3.1 Seeding Model Parameter Query Logic

At step 505, hybrid seed and field data is received by the agriculturalintelligence computer system 130. For example, the communication layer132 of the agricultural intelligence computer system 130 may receivefield data 106 from the field manager computing device 104. Field data106 may include, but is not limited to, specific hybrid seedidentifiers, proposed sowing row width for the specific hybrid seed,geo-location of the user's 102 field, soil properties of the field,climate conditions and micro-climates conditions for the field, andother proposed agricultural strategies.

In an embodiment, the field manager computing device 104 sends fielddata 106. For example, the presentation layer 134 of the agriculturalintelligence computer system 130 may cause display of an interface onfield manager computing device 104 for inputting information, such asthe boundaries of the field, the types of hybrid seed planted, sowingrow width, and other crop and field related information. Thecommunication layer 132 may then receive the field data 106 and relay itto the seeding model parameter query logic 173.

At step 510, a set of digital seeding models is compiled. In anembodiment, the seeding model parameter query logic 173 uses thereceived field data 106 to determine which seeding models are necessaryfor the set of digital seeding models. In an embodiment, field-specificdata associated with a set of test fields is stored in the model andfield data repository 160. The test fields represent measuredagricultural fields where multiple specific hybrid seeds using multiplespecific sowing row widths and seeding rates have been previouslyplanted and field-specific data has been previously collected. A seedingmodel within the set of seeding models includes a dataset of measureddata points including, but not limited to, seeding rate and plant yieldwithin a field, a calculated relationship, such as a linearrelationship, between plant yield and seeding rate, and distributionsrelated to calculation parameters for the relationship. In anembodiment, the size and boundaries of a test field may be based uponestablished CLUs.

In an embodiment, the seeding model parameter query logic 173 extracts atarget hybrid seed and desired seeding row width from the field data106. The seeding model parameter query logic 173 constructs a requestfor multiple seeding models based upon the target hybrid seed type andseeding row width. The request for multiple seeding models refers toseeding models constructed and stored in the model and field datarepository 160. In an embodiment, the seeding model logic 176 createsmultiple seeding models from data points collected from multiple testfields.

A seeding model is a model that describes a marginal relationshipbetween plant yield and seeding rate for a given hybrid seed at aspecific field. The marginal relationship is determined using multiplemeasured data points for a given field and given hybrid seed that hasbeen planted according to a defined sowing row width. In an embodiment,the seeding model logic 176 determines the marginal relationship for agiven hybrid seed and row width using linear regression. Linearregression is an approach for modeling the relationship of per plantyield and seeding rate of a fixed hybrid and field environment. Thisrelationship is modeled between W (Y/ρ, yield per plant in units ofbushels per 1000 seeds) and ρ (seeding rate in units of 1000 seeds peracre), with regression parameters β₀, β₁, and σ. The seeding model mayalso contain dataset distributions for parameters β₀, β₁, and σ basedupon previously observed data. Further details on creating a seedingmodel are discussed in the SEEDING MODEL LOGIC section herein.

In an embodiment, if the model and field data repository 160 does notcontain seeding models matching the request parameters, then the seedingmodel parameter query logic 173 may request that the seeding model logic176 create seeding models from data points collected from multiple testfields.

In an embodiment, data requests may include other matching parameterssuch as geo-location, soil properties, and similar climate conditionsthat are be used to retrieve a filtered set of seeding models that matchthe desired parameters. The model and field data repository 160previously stores measured data points for multiple test fieldsincluding regression and distribution models that are based upon datapoints from the multiple test fields.

3.2 Mixture Model Logic

The agricultural intelligence computer system 130 implements mixturemodel logic 174 to generate an empirical mixture model that utilizesyield and seeding rate information from multiple test fields. At step515, the mixture model logic 174 generates an empirical mixture model,which is a composite distribution of the set of seeding models retrievedby the seeding model parameter query logic 173. A composite distributionis a statistical approach to modeling the distribution of severalseparate distributional populations. In this case, the separatedistributional populations refer to datasets representing multiple testfields. The empirical mixture model contains joint posteriordistributions from the multiple test fields. In an embodiment, eachjoint posterior distribution is represented as θ_(l) whereθ_(l)={β_(l,o),β_(l,1),σ_(l)} and where “l” represents a test field inthe set of test fields, {1, . . . , L}. The empirical mixture modeltherefore may be used to evaluate user 102 field f in terms of its yieldresponse to seeding rate based on those test fields:

p(θ_(f)=θ₀ |I _(f) =l)=p(θ_(l)=θ₀|(Y _(l,1),ρ_(l,1)), . . . ,(Y _(l,n)_(l) ,ρ_(l,n) _(l) )

Where:

p(θ_(f)): is a set of joint posterior distributions for field f that arerepresented as θ_(f)={β_(f,o),β_(f,1),σ_(f)}.

θ₀: is any value within the parameter space.

θ_(l)={β_(l,o),β_(l,1), σ_(l)}: are modeling parameters for eachmeasured field l=1 . . . L.

I_(f): is a membership variable for field f such that p(I_(f)=l)=1/L forany field l.

At step 520, the mixture model logic 174 uses the empirical mixturemodel to calculate an optimal seeding rate distribution for the targethybrid seed and row width. In an embodiment, the mixture model logic 174may implement random sampling techniques such as Monte Carlo sampling toselect which data points that are used to calculate the optimal seedingrate distribution. Monte Carlo sampling is a random sampling approachthat uses a probability distribution to generate sample values. Forexample, random samples are influenced by the mean, median, and standarddeviation values from posterior distributions.

In an embodiment, the optimal seeding rate distribution may bedetermined as an agronomical optimal seeding rate, where the agronomicaloptimal seeding rate ρ_(f) ^(ag) is the seeding rate that maximizes theexpected yield. From the seeding rate model, which is described in theSEEDING MODEL LOGIC section, the agronomical optimal seeding rate can bederived as the negative inverse of β_(f,1) such that,

$\rho_{f}^{ag} = {- {\frac{1}{\beta_{f,1}}.}}$

Therefore the posterior distribution for the agronomical optimal seedingrate of a particular field is represented as a function of β_(f,1) as:

${{\rho_{f}^{ag}}( {Y_{f,1},\rho_{f,1}} )},\ldots \mspace{14mu},{( {Y_{f,n},\rho_{f,n}} ) = {- \frac{1}{ \beta_{f,1} \middle| ( {Y_{f,1},\rho_{f,1}} ) ,\ldots \mspace{14mu},( {Y_{f,n},\rho_{f,n}} )}}}$

In an embodiment, mixture model logic 174 stores the calculated optimalseeding rate distribution in the model and field data repository 160.

3.3 Optimal Seeding Rate Recommendation Logic

The optimal seeding rate recommendation logic 175 is configured todetermine point estimation and an interval estimation of the optimalseeding rate for the given hybrid seed and sowing row width using theoptimal seeding rate distribution. Point estimation of the optimalseeding rate is defined as a seeding rate value that provides either themaximum yield for the given planted hybrid seed or a seeding rate valuethat provides maximum profit for the user 102.

At step 525, the optimal seeding rate recommendation logic 175 evaluatesthe optimal seeding rate distribution and determines the point value foran agronomical optimal seeding rate that provides the user 102 withmaximum yield for his planted hybrid seed crop. In addition, the optimalseeding rate recommendation logic 175 calculates an economical optimalseeding rate that provides the user 102 with maximum profit for hisplanted hybrid seed crop based on seed cost and grain price.

In an embodiment, the point value for the agronomical optimal seedingrate, ρ_(f) ^(ag), is calculated as the median value of the optimalseeding rate distribution. In another embodiment, the mean value of theoptimal seeding rate distribution may be used as the agronomical optimalseeding rate. Using the agronomical optimal seeding rate, the optimalseeding rate recommendation logic 175 further calculates the medianyield for the agronomical optimal seeding rate as:

median Y(ρ_(f) ^(ag))=ρ_(f) ^(ag)×exp(β₀+β₁×ρ_(f) ^(ag))

Where median Y(ρ_(f) ^(ag)) equals the optimal seeding rate ρ_(f) ^(ag)multiplied by the exponential function of β₀ plus β₁×ρ_(f) ^(ag). Themedian yield and agronomical optimal seeding rate provide the user witha seeding rate value that maximizes crop yield and provides estimatedmedian crop yield at that seeding rate. In an embodiment, the optimalseeding rate recommendation logic 175 also calculates variabilityassociated with the agronomical optimal seeding rate as the medianabsolute deviation. Median absolute deviation is a measurement ofvariability for a posterior distribution.

In an embodiment, the optimal seeding rate recommendation logic 175calculates the economical optimal seeding rate as μ_(f) ^(ec) as theseeding rate that maximizes the grain price, multiplied by the medianyield minus the cost (seed price multiplied by seeding rate), such that:

p _(f) ^(ec)=arg max_(ρ)(p _(g)×median Y(ρ)−p _(s)×ρ)

Where:

arg max_(ρ) (p_(g)×median Y(ρ)−p_(s)×ρ): is the value of seeding rate ρthat maximizes the given function, i.e. (p_(g)×median Y(ρ)−p_(s)×ρ).

p_(g): is the grain price represented in dollars per bushel ($/bu).

p_(s): is the seed price represented in dollars per 1000 seeds ($/1000seeds).

The economical optimal seeding rate may differ from the agronomicaloptimal seeding rate because the economical optimal seeding rate isdependent upon grain and seed price. For example, if the price ofpurchasing seed is relatively high and the grain sales price isrelatively low, then from an economical perspective producing themaximum amount of corn yield may not result in maximum profit. Thereforeit is beneficial for the user 102 to be presented with both theagronomical optimal seeding rate and the economical optimal seedingrate. In an embodiment, the point estimates of agronomical optimalseeding rate and the economical optimal seeding rate, together withtheir variability estimations such as the median absolute deviation arecommunicated to the communication layer 132, which then presents theoptimal seeding rate values to the field manager computing device 104for the user 102 to access.

3.4 Seeding Model Logic

The seeding model is a model that describes a marginal relationshipbetween plant yield and seeding rate for a given hybrid seed at aspecific field. FIG. 6 depicts an embodiment of the process by which theseeding model logic 176 creates a seeding model for a specific testfield. At step 602 the seeding model logic 176 queries the model andfield data repository 160 for multiple data points on test fieldscorresponding to the target hybrid seed and row width desired by theuser 102. The model and field data repository 160 then returns a datasetof the requested multiple data points organized by test field. Thepurpose of organizing the data into test field datasets is that eachtest field may have other properties that affect the yield outcomedifferently. Grouping the data points into test field datasets minimizesthe effects of unknown latent variables that may be specific to eachtest field.

In an embodiment, if the model and field data repository 160 does notcontain specific data points for the multiple test fields, then theagricultural intelligence computer system 130 may retrieve the data fromone or more external data server computers 108. An example of specificdata retrieved from the external data server computer 108 is externaldata 110, as depicted in FIG. 1. The external data 110 is received bythe communication layer 132 and then stored in the model and field datarepository 160 to be used by the seeding model logic 176.

At step 604, the seeding model logic 176 creates a linear regressionmodel for each test field dataset. In an embodiment, the seeding modellogic 176 implements a linear regression model based upon Duncan'sexponential function. Duncan's exponential function defines a linearrelationship between the logarithm of the average yield per plant andthe population density of plants. In this case, population density ofplants is measured by the seeding rate and row width of planted seedswithin a field. Notation for measuring the plant yield and seeding rateare as follows:

Y: yield per area in units of bushels per acre;

ρ: seeding rate in units of 1000 seeds per acre.

W: Y/ρ, yield per plant in units of bushels per 1000 seeds (or plants).

Duncan's exponential function models the logarithmic relationship as:

log(W)=β₀+β₁×ρ+ε

Where:

ε is an error term that is based upon a normal error distribution suchas

(0, σ²).

β₀ and β₁ are regression coefficients.

FIGS. 7A and 7B depict the marginal relationship between yield andseeding rate. FIG. 7A illustrates that a parabolic relationship existsbetween yield, measured in bushels per acre (bu/ac), and seeding rate,measured as 1000 seeds planted per acre. Graph 702 depicts data pointscollected for a specific hybrid seed type from various locations withinfield “IA17” during a growing season where seeds were planted using arow width of 20 inches. The horizontal axis represents the seeding rate(1000 seeds/ac) and the vertical axis represents the corn yield (bu/ac).Graph 704 and 706 each depict data points from fields “IL35” during thesame growing season and “MN63” during the following growing seasonrespectively. FIG. 7B depicts the same data as in FIG. 7A but the y-axisrepresents the log yield per plant instead of yield per acre andhighlights a linear relationship between the log yield per plant and theseeding rate.

In an embodiment, the relationship between the corn yield, Y, and theseeding rate, ρ, may be expressed as a log-normal distribution:

Y=

(β₀+β₁×ρ+log_(ρ),σ²)

The log-normal distributions for each measured field are stored within aseeding model.

At step 606, the seeding model logic 176 creates posterior distributionsfor parameters β₀, β₁, and α. A posterior distribution is a normalizeddistribution that takes into account prior probability and observedoutcomes and thereby creates a more informative distribution. In anembodiment, the seeding model logic 176 may impose non-informative priorcalculation such as Jeffrey's prior to determine posterior distributionsfor linear model parameters β and α², where β is the transpose matrix of(β₀, β₁). Jeffrey's prior is a method for imposing a standardnon-informative prior for linear models. A non-informative prior isobjective information related to a variable that provides some basis fordetermining the outcome of that specific variable.

In an embodiment, non-informative prior for regression coefficientsstates that β₁<0 and that joint prior distributions for β and α² assumeproportionality to α, such that:

p(β,σ²)∝1/σ²

where (β, σ²): represents joint prior distributions for β and σ².

In an embodiment, the joint posterior distribution for β is representedas a normal distribution where β is a function of σ² and observationalpairs of corn yield and seeding rate, such that:

β★σ²,(Y ₁,ρ₁), . . . ,(Y _(n),ρ_(n))˜

({circumflex over (β)},σ²(X ^(T) X)⁻¹

where (Y₁,ρ₁), . . . , (Y_(n),ρ_(n)): are n pairs of corn yield andseeding rate observations for a given hybrid seed and field; {circumflexover (β)}: is the estimated β value based upon a seeding rate covariatematrix X and a matrix of observations W,{circumflex over(β)}=(X^(T)X)⁻¹X^(T)W; X: is a covariate matrix of seeding rates, suchthat

${X = \begin{pmatrix}1 & 1_{\ldots} & 1 \\\rho_{1} & \rho_{2} & \rho_{n}\end{pmatrix}^{T}};$

W: are the observations for yield per plant, W=(W₁, . . . , W_(n))^(T);σ²: in posterior follows an inverse gamma distribution of n pairs ofcorn yield and seeding rate observations for a given hybrid seed andfield, such that:

$ \sigma^{2} \middle| ( {Y_{1},\rho_{1}} ) ,\ldots \mspace{14mu},{{ ( {Y_{n},\rho_{n}} ) \sim{Inverse}}\mspace{14mu} {{Gamma}( {\frac{n - 2}{2},\frac{( {n - 2} ){\hat{\sigma}}^{2}}{2}} )}}$

where {circumflex over (σ)}²: is the estimated σ² value,

$\frac{\sum_{i = 1}^{n}( {W_{i} - {\hat{\beta}}_{0} - {{\hat{\beta}}_{1} \times \rho_{i}}} )^{2}}{n - 2}.$

At step 608, the seeding model logic 176 compiles seeding models fordifferent combinations of the given hybrid seed and sowing row width formeasured test fields, where each seeding model corresponds to a singletest field. Each seeding model includes retrieved data points on thetest field, the log-normal distribution created using Duncan'sexponential function, and the joint posterior distributions calculatedusing Jeffrey's prior method. The compiled seeding models are thenstored in the model and field database 160.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction.

What is claimed is:
 1. A computer-implemented method of determining andpresenting an improved seeding rate recommendation for sowing plantseeds in a field, the method comprising: using seeding query logic in aserver computer system, receiving digital data representing plantingparameters comprising hybrid seed type information and planting rowwidth; using the seeding query logic, retrieving a set of one or moredigital seeding models from an electronic digital seeding datarepository based upon the planting parameters, wherein the one or moreseeding models each contain a regression model for the hybrid seed typemodeling a relationship between plant yield and seeding rate at aspecific field; using mixture model logic in the server computer system,generating an empirical mixture model in digital computer memory basedupon the one or more digital seeding models, wherein the empiricalmixture model is a composite distribution of the one or more digitalseeding models; using the mixture model logic, generating an optimalseeding rate distribution dataset in the digital computer memory basedupon the empirical mixture model, wherein the optimal seeding ratedistribution dataset represents the optimal seeding rate for allmeasured field; using optimal seeding rate recommendation logic in theserver computer system, calculating and presenting on a digital displaydevice an optimal seeding rate recommendation based upon the optimalseeding rate distribution dataset.
 2. The method of claim 1, wherein theplanting parameters further comprise soil property data, climatologydata related to a climate at or near a geographic location of the field,and geo-location data specifying a geographic of the field.
 3. Themethod of claim 1, wherein the regression model for the hybrid seed typeis based upon one or more data points measured at the specific field. 4.The method of claim 3, wherein the one or more data points measured atthe specific field comprises digital data representing the hybrid seedtype, the plant yield, and the seeding rate of the hybrid seed planted.5. The method of claim 3, wherein the regression model for the hybridseed type comprises a log-normal distribution of the relationshipbetween plant yield and seeding rate at the specific field.
 6. Themethod of claim 3, wherein the seeding model further comprises jointposterior distributions that represent distributions of regressionparameters used to calculate the regression model.
 7. The method ofclaim 1, wherein generating the optimal seed rate distribution datasetis based upon a negative inverse of parameter values selected from theempirical mixture model.
 8. The method of claim 6, wherein generatingthe optimal seed rate distribution dataset further comprises applying arandom sampling generator to select values from the empirical mixturemodel for evaluation in generating the optimal seeding rate distributiondataset.
 9. The method of claim 8, wherein the random sampling generatoruses Monte Carlo sampling to select values from the empirical mixturemodel.
 10. The method of claim 1, wherein calculating the optimalseeding rate recommendation further comprises determining a median yieldfor the optimal seed rate distribution dataset.
 11. The method of claim1, wherein presenting the optimal seeding rate recommendation furthercomprises presenting variability associated with the seeding raterecommendation, where the variability is characterized as medianabsolute deviation.
 12. One or more non-transitory storage media storinginstructions which, when executed by one or more computing devices,cause performance of a method comprising the steps of: using seedingquery logic in a server computer system, receiving digital datarepresenting planting parameters comprising hybrid seed type informationand planting row width; using the seeding query logic, retrieving a setof one or more digital seeding models from an electronic digital seedingdata repository based upon the planting parameters, wherein the one ormore seeding models each contain a regression model for the hybrid seedtype modeling a relationship between plant yield and seeding rate at aspecific field; using mixture model logic in the server computer system,generating an empirical mixture model in digital computer memory basedupon the one or more digital seeding models, wherein the empiricalmixture model is a composite distribution of the one or more digitalseeding models; using the mixture model logic, generating an optimalseeding rate distribution dataset in the digital computer memory basedupon the empirical mixture model, wherein the optimal seeding ratedistribution dataset represents the optimal seeding rate for allmeasured field; using optimal seeding rate recommendation logic in theserver computer system, calculating and presenting on a digital displaydevice an optimal seeding rate recommendation based upon the optimalseeding rate distribution dataset.
 13. The one or more non-transitorystorage media of claim 12, wherein the planting parameters furthercomprise soil property data, climatology data related to a climate at ornear a geographic location of the field, and geo-location dataspecifying a geographic of the field.
 14. The one or more non-transitorystorage media of claim 12, wherein the regression model for the hybridseed type is based upon one or more data points measured at the specificfield.
 15. The one or more non-transitory storage media of claim 14,wherein the one or more data points measured at the specific fieldcomprises digital data representing the hybrid seed type, the plantyield, and the seeding rate of the hybrid seed planted.
 16. The one ormore non-transitory storage media of claim 14, wherein the regressionmodel for the hybrid seed type comprises a log-normal distribution ofthe relationship between plant yield and seeding rate at the specificfield.
 17. The one or more non-transitory storage media of claim 14,wherein the seeding model further comprises joint posteriordistributions that represent distributions of regression parameters usedto calculate the regression model.
 18. The one or more non-transitorystorage media of claim 12, wherein generating the optimal seed ratedistribution dataset is based upon a negative inverse of parametervalues selected from the empirical mixture model.
 19. The one or morenon-transitory storage media of claim 18, wherein generating the optimalseed rate distribution dataset further comprises applying a randomsampling generator to select values from the empirical mixture model forevaluation in generating the optimal seeding rate distribution dataset;and the random sampling generator uses Monte Carlo sampling to selectvalues from the empirical mixture model.
 20. The one or morenon-transitory storage media of claim 12, wherein calculating theoptimal seeding rate recommendation further comprises determining amedian yield for the optimal seed rate distribution; or presenting theoptimal seeding rate recommendation further comprises presentingvariability associated with the seeding rate recommendation, where thevariability is characterized as median absolute deviation.